This sample demonstrates how a "task mode" agent can act as a sub-agent to an LLM agent, effectively extracting structured data from a conversational flow.

The main agent (coordinator) delegates interactions to two sub-agents:

1. order_collector: A task agent that collects the user's food order (from a menu of Pizza, Burger, Salad) and returns a structured list of selected items as a list[OrderItem].
2. payment_collector: A task agent that collects the user's credit card and CVV information, returning a PaymentInfo object.

Once the tasks are completed, the coordinator automatically uses a place_order tool with the structured data returned by both agents.

• I would like to order some food please.
• I want 2 pizzas and 1 salad.
• My credit card is 1234-5678-9012-3456 and my CVV is 123.

               [ coordinator ] --(uses)--> [ place_order (tool) ]
              /               \
             v                 v
   [ order_collector ]  [ payment_collector ]

1. Define a sub-agent with mode="task" and an output schema:

   order_collector = Agent(
       name="order_collector",
       mode="task",
       output_schema=list[OrderItem],
       ...
   )

2. Assign it to a parent agent and use it in the instruction to collect the information:

   coordinator = Agent(
       sub_agents=[order_collector],
       instruction="Delegate using `order_collector`...",
       ...
   )